{"cells":[{"cell_type":"code","metadata":{"source_hash":"d9c1341a","execution_start":1708810854422,"execution_millis":13828,"deepnote_to_be_reexecuted":false,"cell_id":"4f28aef8322e4d2183574db75410598f","deepnote_cell_type":"code"},"source":"!pip install statsmodels==0.14.1\n!pip install geopy\n!pip install googletrans","block_group":"4f28aef8322e4d2183574db75410598f","execution_count":1,"outputs":[{"name":"stdout","text":"Collecting statsmodels==0.14.1\n  Downloading statsmodels-0.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.8 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m34.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: packaging>=21.3 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from statsmodels==0.14.1) (21.3)\nCollecting patsy>=0.5.4\n  Downloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n\u001b[2K     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/shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas!=2.1.0,>=1.0->statsmodels==0.14.1) (2022.5)\nRequirement already satisfied: python-dateutil>=2.8.2 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from pandas!=2.1.0,>=1.0->statsmodels==0.14.1) (2.8.2)\nRequirement already satisfied: six in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from patsy>=0.5.4->statsmodels==0.14.1) (1.16.0)\nInstalling collected packages: patsy, statsmodels\nSuccessfully installed patsy-0.5.6 statsmodels-0.14.1\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\nCollecting geopy\n  Downloading geopy-2.4.1-py3-none-any.whl (125 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.4/125.4 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting geographiclib<3,>=1.52\n  Downloading geographiclib-2.0-py3-none-any.whl (40 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: geographiclib, geopy\nSuccessfully installed geographiclib-2.0 geopy-2.4.1\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\nCollecting googletrans\n  Downloading googletrans-3.0.0.tar.gz (17 kB)\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hCollecting httpx==0.13.3\n  Downloading httpx-0.13.3-py3-none-any.whl (55 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.1/55.1 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting hstspreload\n  Downloading hstspreload-2024.2.1-py3-none-any.whl (1.1 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m90.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: certifi in /shared-libs/python3.9/py/lib/python3.9/site-packages (from httpx==0.13.3->googletrans) (2022.9.24)\nCollecting httpcore==0.9.*\n  Downloading httpcore-0.9.1-py3-none-any.whl (42 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: sniffio in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from httpx==0.13.3->googletrans) (1.3.0)\nCollecting chardet==3.*\n  Downloading chardet-3.0.4-py2.py3-none-any.whl (133 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.4/133.4 kB\u001b[0m \u001b[31m41.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting rfc3986<2,>=1.3\n  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\nCollecting idna==2.*\n  Downloading idna-2.10-py2.py3-none-any.whl (58 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting h11<0.10,>=0.8\n  Downloading h11-0.9.0-py2.py3-none-any.whl (53 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting h2==3.*\n  Downloading h2-3.2.0-py2.py3-none-any.whl (65 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.0/65.0 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting hpack<4,>=3.0\n  Downloading hpack-3.0.0-py2.py3-none-any.whl (38 kB)\nCollecting hyperframe<6,>=5.2.0\n  Downloading hyperframe-5.2.0-py2.py3-none-any.whl (12 kB)\nBuilding wheels for collected packages: googletrans\n  Building wheel for googletrans (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for googletrans: filename=googletrans-3.0.0-py3-none-any.whl size=15736 sha256=a55695f0b961269a207005e5d824f139043d36d8178c0dc87cc4455f001e984e\n  Stored in directory: /root/.cache/pip/wheels/27/f3/32/d4859d40071f07a5df0ab6fdc0076e78a8a786625dde2b4b2f\nSuccessfully built googletrans\nInstalling collected packages: rfc3986, hyperframe, hpack, h11, chardet, idna, hstspreload, h2, httpcore, httpx, googletrans\n  Attempting uninstall: idna\n    Found existing installation: idna 3.4\n    Not uninstalling idna at /shared-libs/python3.9/py-core/lib/python3.9/site-packages, outside environment /root/venv\n    Can't uninstall 'idna'. No files were found to uninstall.\nSuccessfully installed chardet-3.0.4 googletrans-3.0.0 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2024.2.1 httpcore-0.9.1 httpx-0.13.3 hyperframe-5.2.0 idna-2.10 rfc3986-1.5.0\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n","output_type":"stream"}],"outputs_reference":"s3:deepnote-cell-outputs-production/db327cfa-589c-4c37-821f-547a88b10ee7"},{"cell_type":"code","metadata":{"source_hash":"a61a3b2","execution_start":1708810868268,"execution_millis":7335,"deepnote_to_be_reexecuted":false,"cell_id":"cba2e9660e164f8eae3b558306c20ad0","deepnote_cell_type":"code"},"source":"!pip install statsmodels\n!pip install catboost\nimport statsmodels as sm","block_group":"ecd3a04663944c7484dadea5ab8dd261","execution_count":2,"outputs":[{"name":"stdout","text":"Requirement already satisfied: statsmodels in /root/venv/lib/python3.9/site-packages (0.14.1)\nRequirement already satisfied: scipy!=1.9.2,>=1.4 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from statsmodels) (1.9.3)\nRequirement already satisfied: patsy>=0.5.4 in /root/venv/lib/python3.9/site-packages (from statsmodels) (0.5.6)\nRequirement already satisfied: pandas!=2.1.0,>=1.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from statsmodels) (2.1.4)\nRequirement already satisfied: packaging>=21.3 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from statsmodels) (21.3)\nRequirement already satisfied: numpy<2,>=1.18 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from statsmodels) (1.23.4)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from packaging>=21.3->statsmodels) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.8.2 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.8.2)\nRequirement already satisfied: tzdata>=2022.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2022.5)\nRequirement already satisfied: pytz>=2020.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2022.5)\nRequirement already satisfied: six in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\nCollecting catboost\n  Downloading catboost-1.2.3-cp39-cp39-manylinux2014_x86_64.whl (98.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.5/98.5 MB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: plotly in /shared-libs/python3.9/py/lib/python3.9/site-packages (from catboost) (5.10.0)\nRequirement already satisfied: numpy>=1.16.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from catboost) (1.23.4)\nRequirement already satisfied: scipy in /shared-libs/python3.9/py/lib/python3.9/site-packages (from catboost) (1.9.3)\nCollecting graphviz\n  Downloading graphviz-0.20.1-py3-none-any.whl (47 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.0/47.0 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: six in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from catboost) (1.16.0)\nRequirement already satisfied: matplotlib in /shared-libs/python3.9/py/lib/python3.9/site-packages (from catboost) (3.6.0)\nRequirement already satisfied: pandas>=0.24 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from catboost) (2.1.4)\nRequirement already satisfied: python-dateutil>=2.8.2 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from pandas>=0.24->catboost) (2.8.2)\nRequirement already satisfied: tzdata>=2022.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas>=0.24->catboost) (2022.5)\nRequirement already satisfied: pytz>=2020.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas>=0.24->catboost) (2022.5)\nRequirement already satisfied: fonttools>=4.22.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->catboost) (4.37.4)\nRequirement already satisfied: cycler>=0.10 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->catboost) (0.11.0)\nRequirement already satisfied: packaging>=20.0 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->catboost) (21.3)\nRequirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->catboost) (1.4.4)\nRequirement already satisfied: contourpy>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->catboost) (1.0.5)\nRequirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->catboost) (3.0.9)\nRequirement already satisfied: pillow>=6.2.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->catboost) (9.2.0)\nRequirement already satisfied: tenacity>=6.2.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from plotly->catboost) (8.1.0)\nInstalling collected packages: graphviz, catboost\nSuccessfully installed catboost-1.2.3 graphviz-0.20.1\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n","output_type":"stream"}],"outputs_reference":"s3:deepnote-cell-outputs-production/7f96b798-0a9b-483c-8023-9fedde7deb5b"},{"cell_type":"code","metadata":{"source_hash":"6ce60a0a","execution_start":1708810875606,"execution_millis":938,"deepnote_to_be_reexecuted":false,"cell_id":"8020d71d78914895bf7b10f095bdcfc4","deepnote_cell_type":"code"},"source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn\nimport statsmodels.api as sm\nimport scipy as sp\nimport requests\nfrom bs4 import BeautifulSoup\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","block_group":"57695412b43b4aecaa1b37e03cae68d8","execution_count":3,"outputs":[],"outputs_reference":null},{"cell_type":"markdown","metadata":{"formattedRanges":[{"url":"https://earthquaketrack.ru/country/ru/","type":"link","ranges":[],"toCodePoint":74,"fromCodePoint":36},{"url":"https://www.kaggle.com/datasets/usgs/earthquake-database","type":"link","ranges":[],"toCodePoint":133,"fromCodePoint":77}],"cell_id":"9628a1c3f1454bc69e2ff3996af9751f","deepnote_cell_type":"text-cell-p"},"source":"Мы взяли данные о землетрясеньях из https://earthquaketrack.ru/country/ru/ и https://www.kaggle.com/datasets/usgs/earthquake-database. Также большинство данных мы добавили вручную \"искусственно\" на основе уже имеющихся","block_group":"63848230ff2c426f82f10f384cb9961d"},{"cell_type":"code","metadata":{"source_hash":"9c337ecd","execution_start":1708810994818,"execution_millis":246,"deepnote_to_be_reexecuted":false,"cell_id":"15dedb251cb8482d8606ac9353322666","deepnote_cell_type":"code"},"source":"data = pd.read_csv('database5.csv')\ndata = data.drop('Unnamed: 0',axis=1)","block_group":"b5bb8ceaa8c748cfa2d93f9cb78938f5","execution_count":9,"outputs":[],"outputs_reference":null},{"cell_type":"code","metadata":{"source_hash":"2d53ad03","execution_start":1708810997576,"execution_millis":337,"deepnote_to_be_reexecuted":false,"cell_id":"f9a4756046f24a058c073d963986242d","deepnote_cell_type":"code"},"source":"sns.set_style('whitegrid')\nsns.set(style=\"whitegrid\")\ncorr = data.corr()\nmask = np.zeros_like(corr, dtype=np.bool_)\nmask[np.triu_indices_from(mask)] = True\nf, ax = plt.subplots(figsize=(7, 5))\ncmap = 'Purples'\nsns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\nplt.show()","block_group":"3358f6b2d3af4f98a6c807e8b26f6657","execution_count":10,"outputs":[{"data":{"text/plain":"<Figure size 700x500 with 2 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":593,"height":506}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/53caf3d2-7df8-4e23-afb4-74b064e87c56"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"9c5f51b9c7c746c0a8ea5ab59319c1d6","deepnote_cell_type":"text-cell-h1"},"source":"# Обучение","block_group":"7ae7f00110264a3cafb0ff8407ca3495"},{"cell_type":"code","metadata":{"source_hash":"e38ba4ae","execution_start":1708808300827,"execution_millis":61,"deepnote_to_be_reexecuted":true,"cell_id":"4285037ed9d647259299de7a867641c4","deepnote_cell_type":"code"},"source":"import statsmodels.api as sm\nX =  data.drop(['Магнитуда','Глубина'], axis=1)\nX = sm.add_constant(X)\ny = data['Магнитуда']\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=51)","block_group":"d472b77b99434c088bf1956469415545","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"0e209b4183c0427ca5b7237c35595364","deepnote_cell_type":"text-cell-p"},"source":"RandomForestRegressor","block_group":"8c8bb8eda65143aea1640fd318e7df19"},{"cell_type":"code","metadata":{"source_hash":"426b9d24","execution_start":1708808307446,"execution_millis":195305,"deepnote_to_be_reexecuted":true,"cell_id":"f8b63fbedab2491f86ac11694fc38bdc","deepnote_cell_type":"code"},"source":"from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, BaggingRegressor\nfrom sklearn import preprocessing\nfrom sklearn.metrics import mean_squared_error\n\nrfc = RandomForestRegressor(n_estimators=600,random_state=42)\nresults_rfc = rfc.fit(X_train, y_train)\ny_pred_rfc = results_rfc.predict(X_test)","block_group":"5fd47842366143b28ee13fbb538b92d3","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"bf12ec9eb5df4c0982cb11827ef150c8","deepnote_cell_type":"text-cell-p"},"source":"CatBoost","block_group":"eecc632bdc4a410a8fc5745d9c72da98"},{"cell_type":"code","metadata":{"source_hash":"e428a04b","execution_start":1708791886259,"execution_millis":30982,"deepnote_to_be_reexecuted":true,"cell_id":"1a49891a4ce04ade906fc426084c7a17","deepnote_cell_type":"code"},"source":"import catboost as cb\nfrom sklearn.metrics import mean_squared_error\n\nmodel_cb = cb.CatBoostRegressor(\n    iterations=2000,\n    depth=8,\n    loss_function='RMSE',\n    verbose=False\n)\n\ncategorical_features_indices = np.where(X_train.dtypes == 'category')[0]\nresults_cb = model_cb.fit(\n    X_train, y_train, \n    cat_features=categorical_features_indices\n)\n\ny_pred_cb = results_cb.predict(X_test)","block_group":"540954894cdb4f2d82b757cf677b3943","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"3fdfc1c4b055445fbd1baaae8aa4be6f","deepnote_cell_type":"text-cell-p"},"source":" DecisionTree","block_group":"8cf0cea85fcb4798bea230a1d63d4646"},{"cell_type":"code","metadata":{"source_hash":"b1a426ca","execution_start":1708790502955,"execution_millis":200,"deepnote_to_be_reexecuted":true,"cell_id":"7ae8dddbb3424800961cbb765810ea6c","deepnote_cell_type":"code"},"source":"from sklearn.tree import DecisionTreeRegressor\nmodel_tree = DecisionTreeRegressor(max_depth=6, random_state=51)\nresults_tree = model_tree.fit(X_train, y_train)\ny_pred_tree = results_tree.predict(X_test)","block_group":"50df8d16cca043199d84038b7a379da1","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"7847ff66968d47098a8431470ca9abb0","deepnote_cell_type":"text-cell-p"},"source":"OLS","block_group":"a82ff38bf9bd4da6a592895e3b8bf082"},{"cell_type":"code","metadata":{"source_hash":"53533336","execution_start":1708790503158,"execution_millis":148,"deepnote_to_be_reexecuted":true,"cell_id":"77c027b5435944ddb09c9cbf649179cb","deepnote_cell_type":"code"},"source":"model_OLS = sm.OLS(y_train, X_train)\nresults_OLS = model_OLS.fit()\ny_pred_OLS = results_OLS.predict(X_test)","block_group":"43b3bc5952484a18aff810312b2e44d9","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"337bf1788c22463bbabc75e973dd8db1","deepnote_cell_type":"text-cell-h1"},"source":"# Метрики","block_group":"73e52f1264cb47129db262d0dda45d7b"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"f899f52adcfa4062a5df363158c21f4b","deepnote_cell_type":"text-cell-p"},"source":"MAE","block_group":"ac17a483783d4748accb64675e379690"},{"cell_type":"code","metadata":{"source_hash":"7c92a59e","execution_start":1708792046421,"execution_millis":27,"deepnote_to_be_reexecuted":true,"cell_id":"287e301e59b84b44820e1279f599aa09","deepnote_cell_type":"code"},"source":"from sklearn.metrics import mean_absolute_error\nprint(\"DecisionTree  \", mean_absolute_error(y_test, y_pred_tree))\nprint(\"OLS    \", mean_absolute_error(y_test, y_pred_OLS))\nprint(\"CatBoost  \", mean_absolute_error(y_test, y_pred_cb))\nprint(\"RandomForest   \", mean_absolute_error(y_test, y_pred_rfc))","block_group":"154ddeeb0a1c4290a5663817ce711a55","execution_count":null,"outputs":[{"name":"stdout","text":"DecisionTree   0.3834038058217566\nOLS     1.6501382093985941\nCatBoost   0.25616504997096784\nRandomForest    0.20679368090165023\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/5e648068-9509-484a-9ea1-f66f2924e03e"},{"cell_type":"code","metadata":{"source_hash":"89646348","execution_start":1708790503411,"execution_millis":197,"deepnote_to_be_reexecuted":true,"cell_id":"5ad1f1fb789e4d69b3ef576ca462fc8f","deepnote_cell_type":"code"},"source":"plt.title('MAE')\nplt.xlabel('Модели')\nplt.ylabel('Ошибки')\nindex = [\"LinReg\",\"DecisionTree\",\"RandomTree\",\"CatBoost\"]\nvalues = [mean_absolute_error(y_test, y_pred_OLS), mean_absolute_error(y_test, y_pred_tree), mean_absolute_error(y_test, y_pred_rfc), mean_absolute_error(y_test, y_pred_cb)]\nsns.set_theme(palette='light:#83F_r',style='ticks',rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\nsns.barplot(x=index,y=values)","block_group":"b6a1d3ee3a8946b39918f0cd818e528e","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"<AxesSubplot: title={'center': 'MAE'}, xlabel='Модели', ylabel='Ошибки'>"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":577,"height":463}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/29345985-8b3a-4c6b-8ec8-00e01325e485"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"aedb681e6f784bfab6755c7baf842c94","deepnote_cell_type":"text-cell-p"},"source":"MSE","block_group":"2e153f03a808472ba6b7796e6e9090b1"},{"cell_type":"code","metadata":{"source_hash":"a64f7796","execution_start":1708790503739,"execution_millis":288,"deepnote_to_be_reexecuted":true,"cell_id":"8a5ed7aa6f394a48a5beb5a54a0870c7","deepnote_cell_type":"code"},"source":"from sklearn.metrics import mean_squared_error\nprint(\"DecisionTree  \", mean_squared_error(y_test, y_pred_tree))\nprint(\"OLS   \", mean_squared_error(y_test, y_pred_OLS))\nprint(\"CatBoost   \", mean_squared_error(y_test, y_pred_cb))\nprint(\"RandomForest   \", mean_squared_error(y_test,y_pred_rfc))","block_group":"c7c4b95eba1344c2a51bc35c89232aad","execution_count":null,"outputs":[{"name":"stdout","text":"DecisionTree   1.0037460760212575\nOLS    4.831585209364136\nCatBoost    0.5348175613392805\nRandomForest    0.48142590647989814\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/93611899-29ea-492f-a265-e7b871f80485"},{"cell_type":"code","metadata":{"source_hash":"a7cc985c","execution_start":1708790503739,"execution_millis":289,"deepnote_to_be_reexecuted":true,"cell_id":"8c50f7d70fae4ccba6a6008f3784872d","deepnote_cell_type":"code"},"source":"plt.title('MSE')\nplt.xlabel('Модели')\nplt.ylabel('Ошибки')\nindex = [\"LinReg\",\"DecisionTree\",\"RandomTree\",\"CatBoost\"]\nvalues = [mean_squared_error(y_test, y_pred_OLS), mean_squared_error(y_test, y_pred_tree), mean_squared_error(y_test, y_pred_rfc), mean_squared_error(y_test, y_pred_cb)]\nsns.set_theme(palette='light:#83F_r',style='ticks',rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\nsns.barplot(x=index,y=values)","block_group":"b69140b543534a33be001abf67f2c5f4","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"<AxesSubplot: title={'center': 'MSE'}, xlabel='Модели', ylabel='Ошибки'>"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":562,"height":463}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/b148754d-f7e7-45aa-8768-f9a84a0ffbb4"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"b9786ed4249c4e6badce5e8cc78985d7","deepnote_cell_type":"text-cell-p"},"source":"RMSE","block_group":"3d75bc72a53749e78b2c6d8a28d03a55"},{"cell_type":"code","metadata":{"source_hash":"b58125b","execution_start":1708790503995,"execution_millis":227,"deepnote_to_be_reexecuted":true,"cell_id":"3c294838e3b945b58c830d48080ab24f","deepnote_cell_type":"code"},"source":"from sklearn.metrics import mean_squared_error\nprint(\"DecisionTree   \", np.sqrt(mean_squared_error(y_test, y_pred_tree)))\nprint(\"OLS   \", np.sqrt(mean_squared_error(y_test, y_pred_OLS)))\nprint(\"CatBoost   \", np.sqrt(mean_squared_error(y_test, y_pred_cb)))\nprint(\"RandomForest   \", np.sqrt(mean_squared_error(y_test, y_pred_rfc)))","block_group":"a95f2efc282f45e39c5b568e342f5b30","execution_count":null,"outputs":[{"name":"stdout","text":"DecisionTree    1.0018712871528246\nOLS    2.198086715615227\nCatBoost    0.7313122187816093\nRandomForest    0.6938486192822597\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/5efd2468-4114-4240-8e01-9120729f8de5"},{"cell_type":"code","metadata":{"source_hash":"611ff715","execution_start":1708790503996,"execution_millis":226,"deepnote_to_be_reexecuted":true,"cell_id":"cdb4f0edba9946ce9b448b7eefc922d1","deepnote_cell_type":"code"},"source":"plt.title('RMSE')\nplt.xlabel('Модели')\nplt.ylabel('Ошибки')\nindex = [\"LinReg\",\"DecisionTree\",\"RandomTree\",\"CatBoost\"]\nvalues = [mean_squared_error(y_test, y_pred_OLS), mean_squared_error(y_test, y_pred_tree), mean_squared_error(y_test, y_pred_rfc), mean_squared_error(y_test, y_pred_cb)]\nsns.set_theme(palette='light:#83F_r',style='ticks',rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\nsns.barplot(x=index,y=values)","block_group":"add23e5932f74a5980fbe5e9f9327017","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"<AxesSubplot: title={'center': 'RMSE'}, xlabel='Модели', ylabel='Ошибки'>"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":562,"height":463}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/da8bc14a-2369-4f03-a20a-5c776cba1a40"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"fe4416082c374b678c5916ecfe993c77","deepnote_cell_type":"text-cell-p"},"source":"MAPE","block_group":"38395a313a9d455ab4f316da0790a738"},{"cell_type":"code","metadata":{"source_hash":"5693c7f8","execution_start":1708790504255,"execution_millis":267,"deepnote_to_be_reexecuted":true,"cell_id":"72c98dbc635540acb07d53ea17cf44cf","deepnote_cell_type":"code"},"source":"from sklearn.metrics import mean_absolute_percentage_error\nprint(\"DecisionTree   \", mean_absolute_percentage_error(y_test, y_pred_tree))\nprint(\"OLS   \",mean_absolute_percentage_error(y_test, y_pred_OLS))\nprint(\"CatBoost   \", mean_absolute_percentage_error(y_test, y_pred_cb))\nprint(\"RandomForest   \", mean_absolute_percentage_error(y_test, y_pred_rfc))","block_group":"6382a0c5f4374c32968f9a10240fe3d8","execution_count":null,"outputs":[{"name":"stdout","text":"DecisionTree    778105846513191.0\nOLS    3728861174719027.5\nCatBoost    534394408211029.4\nRandomForest    385993625857530.1\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/cd0e8031-8857-469f-b6a5-ff5a028753e6"},{"cell_type":"code","metadata":{"source_hash":"f574fae7","execution_start":1708790504255,"execution_millis":267,"deepnote_to_be_reexecuted":true,"cell_id":"46f71e7330db47ad800a55929ed1e9bc","deepnote_cell_type":"code"},"source":"plt.title('MAPE')\nplt.xlabel('Модели')\nplt.ylabel('Ошибки')\nindex = [\"LinReg\",\"DecisionTree\",\"RandomTree\",\"CatBoost\"]\nvalues = [mean_absolute_percentage_error(y_test, y_pred_OLS), mean_absolute_percentage_error(y_test, y_pred_tree), mean_absolute_percentage_error(y_test, y_pred_rfc), mean_absolute_percentage_error(y_test, y_pred_cb)]\nsns.set_theme(palette='light:#83F_r',style='ticks',rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\nsns.barplot(x=index,y=values)","block_group":"6324f68c04ff42e18a3f9c203bf29aa5","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"<AxesSubplot: title={'center': 'MAPE'}, xlabel='Модели', ylabel='Ошибки'>"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":577,"height":463}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/7f88b95d-a25f-4d8f-b1c4-7b2691d58fcc"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"06f2320c35124abe80aaca9e55a11ca7","deepnote_cell_type":"text-cell-h1"},"source":"# Работа с пользовательским запросом","block_group":"604941d815da4c1883bc70030c157476"},{"cell_type":"code","metadata":{"source_hash":"cbbe320e","execution_start":1708793179262,"execution_millis":62308,"deepnote_to_be_reexecuted":true,"cell_id":"7911a811797d4c66b69e568ae59fd90d","deepnote_cell_type":"code"},"source":"import datetime\nlatitude = -350\nlongitude = -350\nwhile (latitude<-180 or latitude>180) or (longitude<-180 or longitude>180):\n    latitude = float(input('Введите широту').replace(',','.'))\n    longitude = float(input('Введите долготу').replace(',','.'))\ncurrent_time = datetime.datetime.now()","block_group":"3f3d460aed214a8fbe378992f91901b9","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"code","metadata":{"source_hash":"497aaacd","execution_start":1708793246160,"execution_millis":66,"deepnote_to_be_reexecuted":true,"cell_id":"dd82fddd57c74822a0da621963814f80","deepnote_cell_type":"code"},"source":"s = []\nfor td in range(32):\n    date = current_time + datetime.timedelta(days=td)\n    s.append([1,int(date.day),int(date.month),int(date.year),latitude,longitude])\ndf = pd.DataFrame(s)","block_group":"c9a941bee85346c1b444d509a73e346f","execution_count":null,"outputs":[],"outputs_reference":null},{"cell_type":"code","metadata":{"source_hash":"a1275e89","execution_start":1708793252899,"execution_millis":22,"deepnote_to_be_reexecuted":true,"cell_id":"9e818ec7a9944c638162d22b6720fd9d","deepnote_cell_type":"code"},"source":"y_pred = results_rfc.predict(df)\nans = y_pred.mean()\nif (ans < 3 and y_pred.max() < 4):\n    print(\"Землетрясений не будет\")\nelse:\n    print(\"Будет землятрясение магнитудой:\", round(ans, 2))","block_group":"604f8d946ae148d6863f8e61b1a44a65","execution_count":null,"outputs":[{"name":"stdout","text":"Землетрясений не будет\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/13c8cc5a-8dfd-4d19-a33e-aeb480a3bd6c"},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=afa4e8ab-8d82-4be3-84d5-bca85877b62a' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote_notebook_id":"d9e4cc7526524f309bf240ceab8df319","deepnote_execution_queue":[]}}